SG-AMP: Scene-Graph-Guided Active Perception and Semantics-Aware Motion Planning for Pepper Plants
该研究通过结合深度补全、全景映射、场景图推理及语义感知运动规划，解决了辣椒植株的主动感知与规划问题。
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StrAD: A Streaming Method and Benchmark for Audio Description Generation for Long-form Videos
This work addresses the limitations of existing audio description generation methods, which struggle with long-form videos and rely on manually annotated timestamps, thereby failing to meet the accessibility needs of visually impaired users at scale. The paper introduces the first streaming framework for audio description generation tailored to long videos, leveraging a sliding window mechanism to enable real-time caption insertion without ground-truth timestamps. The framework supports both fine-tuning (StrAD-FT) and zero-shot prompting with vision-language models (StrAD-Zero). Additionally, the authors construct StrAD, a diverse benchmark of long videos, to standardize full-video-level evaluation. Experiments show that the proposed method achieves a CIDEr score of 36.3 on CMD-AD—outperforming prior work by 10.0—and reaches 51.0 CIDEr on the StrAD benchmark, with a streaming task SODA score of 2.4, substantially exceeding the zero-shot baseline of 1.1.
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SG-AMP: Scene-Graph-Guided Active Perception and Semantics-Aware Motion Planning for Pepper Plants
该研究通过结合深度补全、全景映射、场景图推理及语义感知运动规划，解决了辣椒植株的主动感知与规划问题。
0 citationsRead paper
StrAD: A Streaming Method and Benchmark for Audio Description Generation for Long-form Videos
This work addresses the limitations of existing audio description generation methods, which struggle with long-form videos and rely on manually annotated timestamps, thereby failing to meet the accessibility needs of visually impaired users at scale. The paper introduces the first streaming framework for audio description generation tailored to long videos, leveraging a sliding window mechanism to enable real-time caption insertion without ground-truth timestamps. The framework supports both fine-tuning (StrAD-FT) and zero-shot prompting with vision-language models (StrAD-Zero). Additionally, the authors construct StrAD, a diverse benchmark of long videos, to standardize full-video-level evaluation. Experiments show that the proposed method achieves a CIDEr score of 36.3 on CMD-AD—outperforming prior work by 10.0—and reaches 51.0 CIDEr on the StrAD benchmark, with a streaming task SODA score of 2.4, substantially exceeding the zero-shot baseline of 1.1.
0 citationsRead paper